Hierarchical Spatial Feature-CNN Employing Grad-CAM for Enhanced Segmentation and Classification in Alzheimer's and Parkinson's Disease Diagnosis via MRI

The precise diagnosis of neurodegenerative disorders, notably Parkinson's disease (PD) and Alzheimer's disease (AD), presents a formidable challenge, often necessitating several years for definitive determination.Given the increasing prevalence of PD and AD in aging populations of affluent nations, there is an urgent need for advanced technology and more precise diagnostic methodologies, particularly for early disease stages.In recent years, segmentation has seen a surge in application for processing Magnetic Resonance (MR) brain data, emerging as a valuable and indispensable tool.To capture comprehensive images of the brain for the diagnosis and classification of these neurodegenerative disorders, Magnetic Resonance Imaging (MRI) is employed.However, early detection and classification of PD and AD utilizing MRI datasets pose significant complexities.Owing to the inherent subjectivity of human observation, automated segmentation of MRI images has become a crucial asset for healthcare professionals.The primary focus of this study is to devise an effective image segmentation approach and classification techniques for the detection and categorization of AD and PD.Initially, Hierarchical Spatial Feature-CNN is employed to segment abnormal traces of PD and AD in MRIs.Subsequently, the Gradient-weighted Class Activation Mapping (Grad-CAM) method is used for disease classification.In Grad-CAM, each neuron is assigned prioritization weights based on their contribution to the classification of interest, using gradient information flowing into the final convolutional layer of the Convolutional Neural Network (CNN).Thus, the combination of Grad-CAM with CNN is applied to address the classification challenges inherent in PD and AD.Extensive experiments were conducted on the proposed model, resulting in a classification accuracy exceeding 98.17%.In addition, the proposed integration of Grad-CAM with CNN outperformed existing state-of-the-art approaches across all performance measures.This study underscores the potential of the proposed model in enhancing the diagnostic process for neurodegenerative disorders, offering promise for more efficient and accurate detection and classification of PD and AD.